A second approach for UDA is to align the distributions of the two domains directly in a latent embedding space [1]}. A shared encoder is used to generate latent embeddings for both domains and then it is trained such that the distance between the domain-specific distributions is minimized with respect to a suitable probability distance metrics at the output of the shared encoder [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. Selecting the proper distance metrics has been the major focus of research for these approaches. Optimal transport has been found particularly suitable for UDA based on deep learning due to its suitability for gradient-based optimization [8]}. Building our prior work, we benefit from the Sliced Wasserstein distance (SWD) [9]}, [10]} variant of the optimal transport [2]}. SWD has properties similar to optimal transport, but also has a closed form solution that can be used to compute it efficiently.
